Traffic congestion is a major economic issue, costing some municipalities billions of dollars per year. Various adaptive traffic signal control techniques, as opposed to pre-timed and actuated signal control, have been proposed in an attempt to alleviate this problem.
Employing adaptive signal control strategies at a local level (isolated intersections) has been found to limit potential benefits. Therefore, optimally controlling the operation of multiple intersections simultaneously can be synergetic and beneficial. However, such integration typically adds significant complexity to the problem rendering a real time solution infeasible. Two distinct approaches to adaptive signal control include centralized control and decentralized control. Centralised control may limit the scalability and robustness of the overall system due to theoretical and practical issues.
In centralized control, all optimization computations need to be performed at a central computer that resides in a command centre, and as the number of intersections under simultaneous control increases, the dimensionality of the solution space grows exponentially, rendering finding a solution theoretically intractable and computationally infeasible, even for a handful of intersections. In addition, expanding the network could require upgrading the computing power at the control room. Moreover, the central computer ideally needs to communicate in real time, all the time, with all intersections. The required communication network and related cost is prohibitive for many municipalities and challenging for even large municipalities. In addition to communication cost, reliability is another challenge, especially in cases of communication failure between the intersections and the traffic management centre.
Decentralized control, on the other hand, is motivated by the above challenges of centralized control. Existing decentralized control methods, however, currently suffer from several problems. Either each local signal controller (at each intersection) is isolated, acting independently of all surrounding intersections, in which case it will not be responsive to traffic conditions elsewhere in the traffic network, or the local signal controller must obtain and consider traffic conditions from all the other intersections, in which case the problems of centralized control are repeated and exacerbated by lack of computational power at local intersections.
Additionally, most adaptive traffic techniques attempt to optimize an offset parameter (time between the beginning of the green phase of two consecutive traffic signals) but this is mainly effective where all signals have the same cycle (or multiples of cycles). Thus, it is difficult to maintain coordination if cycle lengths or phase splits are sought to vary. For this reason, these coordination techniques are typically employed along an arterial road, where the major demand is, and are not generically designed to cope with any type of traffic network or any traffic demand distribution.
Moreover, many adaptive traffic techniques attempt to optimize the signal timing plans based on models of the traffic environment (that provide system state-transition probabilities) which are difficult to generate because of the uncertainty associated with traffic arrivals and drivers' behaviour at signalized intersections.
Furthermore, many of the existing adaptive traffic signal control systems require highly-skilled labour which is often hard to find, train and retain for small municipalities or even large cities with ample resources. This problem is typical with advanced systems and knowledge-intensive applications. There is a need for considerable expertise to ensure the successful operation and implementation of an adaptive traffic signal control system, which continues to be a major challenge.
For the foregoing reasons, the behaviour of traffic signal networks is not optimized and signals are not coordinated in most existing practical implementations. Instead each signal is independently optimized. Therefore, the signals are, at best, locally optimal but collectively produce suboptimal solutions.
It is an object of the following to mitigate or obviate at least one of the above mentioned disadvantages.